Abstract: To resolve the resource pressure of multi-satellite on-orbit-telemetry in TT&C（Tracking, Telemetry and Control） network, on the basis of analyzing the phenomenon of resource conflict in TT&C network, a resource assignment model of TT&C network was constructed. The model was established by the existing hardware resource in TT&C network. On the basis of not increasing the construction cost of TT&C network, the effectiveness factor was introduced, which not only preferentially met the measuring & controlling demand of higher level satellite, but also fully considered the actual factors in each TT&C station, such as delay and bandwidth, when it received the TT&C mission. Through allocating different weight, the model realized the maximum of the utilization ratio of the existing TT&C resource, and the model was settled by Genetic Algorithm (GA). The results of simulation experiment demonstrate that compared with the traditional resource assignment method, the task accomplishing ratio of the model is improved by 23%, and the utilization ratio of TT&C resource is over two times higher than the traditional method, but the running time of the algorithm is equivalent to the traditional method. Therefore, under the conditions of multi-window and multi-satellite telemetry at the same time, the model can much more reasonably assign the TT&C resource, and improve the utilization ratio of the whole TT&C network.

Key words: Tracking, Telemetry and Control (TT&C), resource assignment, Genetic Algorithm (GA), multi-window and multi-satellite telemetry, simulation

摘要： 为了解决多星在轨测控给航天测控网带来的资源压力，在分析航天测控网资源冲突现象的基础上，构建了一种航天测控网资源分配模型。该模型立足于航天测控网现有的硬件资源，在不增加测控网建设成本的基础上，引入效益因子，既优先满足测控级别较高的卫星的测控需求，又充分考虑接受测控任务时各个测控站的时延和带宽等实际因素，通过分配不同的权重，实现现有测控资源利用率的最大化，并采用遗传算法(GA)对该模型进行求解。通过仿真实验表明，与传统的资源分配方法相比，该模型的任务完成率提高了23%，测控资源利用效率为传统方法的2倍以上，而算法的运行时间与传统的方法相当。因此，在多窗口多星同时测控的条件下，该模型更能合理配置测控资源，提高整个测控网的利用效率。

关键词: 航天测控, 资源分配, 遗传算法, 多窗口多星测控, 仿真